if a splits_final.json exists in the raw dataset folder it will be co…

…pied to the preprocessed folder as part of the experiment planning

nnunetv2/experiment_planning/experiment_planners/default_experiment_planner.py

@@ -1,4 +1,3 @@
-import os.path
 import shutil
 from copy import deepcopy
 from functools import lru_cache
@@ -79,6 +78,10 @@ def __init__(self, dataset_name_or_id: Union[str, int],
 
         self.plans = None
 
+        if isfile(join(self.raw_dataset_folder, 'splits_final.json')):
+            _maybe_copy_splits_file(join(self.raw_dataset_folder, 'splits_final.json'),
+                                    join(preprocessed_folder, 'splits_final.json'))
+
     def determine_reader_writer(self):
         example_image = self.dataset[self.dataset.keys().__iter__().__next__()]['images'][0]
         return determine_reader_writer_from_dataset_json(self.dataset_json, example_image)
@@ -530,5 +533,23 @@ def load_plans(self, fname: str):
         self.plans = load_json(fname)
 
 
+def _maybe_copy_splits_file(splits_file: str, target_fname: str):
+    if not isfile(target_fname):
+        shutil.copy(splits_file, target_fname)
+    else:
+        # split already exists, do not copy, but check that the splits match.
+        # This code allows target_fname to contain more splits than splits_file. This is OK.
+        splits_source = load_json(splits_file)
+        splits_target = load_json(target_fname)
+        # all folds in the source file must match the target file
+        for i in range(len(splits_source)):
+            train_source = set(splits_source[i]['train'])
+            train_target = set(splits_target[i]['train'])
+            assert train_target == train_source
+            val_source = set(splits_source[i]['val'])
+            val_target = set(splits_target[i]['val'])
+            assert val_source == val_target
+
+
 if __name__ == '__main__':
     ExperimentPlanner(2, 8).plan_experiment()

nnunetv2/experiment_planning/plan_and_preprocess_api.py

@@ -1,17 +1,16 @@
-import shutil
 from typing import List, Type, Optional, Tuple, Union
 
-import nnunetv2
-from batchgenerators.utilities.file_and_folder_operations import join, maybe_mkdir_p, subfiles, load_json
+from batchgenerators.utilities.file_and_folder_operations import join, maybe_mkdir_p, load_json
 
+import nnunetv2
+from nnunetv2.configuration import default_num_processes
 from nnunetv2.experiment_planning.dataset_fingerprint.fingerprint_extractor import DatasetFingerprintExtractor
 from nnunetv2.experiment_planning.experiment_planners.default_experiment_planner import ExperimentPlanner
 from nnunetv2.experiment_planning.verify_dataset_integrity import verify_dataset_integrity
 from nnunetv2.paths import nnUNet_raw, nnUNet_preprocessed
-from nnunetv2.utilities.dataset_name_id_conversion import convert_id_to_dataset_name, maybe_convert_to_dataset_name
+from nnunetv2.utilities.dataset_name_id_conversion import convert_id_to_dataset_name
 from nnunetv2.utilities.find_class_by_name import recursive_find_python_class
 from nnunetv2.utilities.plans_handling.plans_handler import PlansManager
-from nnunetv2.configuration import default_num_processes
 from nnunetv2.utilities.utils import get_filenames_of_train_images_and_targets
 
 

nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py

@@ -520,9 +520,9 @@ def plot_network_architecture(self):
     def do_split(self):
         """
         The default split is a 5 fold CV on all available training cases. nnU-Net will create a split (it is seeded,
-        so always the same) and save it as splits_final.pkl file in the preprocessed data directory.
+        so always the same) and save it as splits_final.json file in the preprocessed data directory.
         Sometimes you may want to create your own split for various reasons. For this you will need to create your own
-        splits_final.pkl file. If this file is present, nnU-Net is going to use it and whatever splits are defined in
+        splits_final.json file. If this file is present, nnU-Net is going to use it and whatever splits are defined in
         it. You can create as many splits in this file as you want. Note that if you define only 4 splits (fold 0-3)
         and then set fold=4 when training (that would be the fifth split), nnU-Net will print a warning and proceed to
         use a random 80:20 data split.